Popwear is an AI-powered fashion app designed for the North American market, helping young users aged 18–40 solve their styling challenges. Through its three core features users can explore diverse styles at low cost and gain instant inspiration.
Team
1 Product Manager, 3 Developers
Tools
Figma, Linear, Recraft
CHALLENGE
Fashion users often face two core challenges: not knowing how to style their clothes and not knowing what to wear.
In daily life, they lack effective guidance and often feel overwhelmed by rapidly changing fashion trends.
Although the AI fashion market shows great potential, online adoption remains low and market share is fragmented. Despite a large user base, conversion is still limited, indicating significant untapped business opportunities.
How might we use AI to make outfit decisions effortless and enjoyable for users?
Key Features
Based on the identified user and business pain points, we defined three core features for the MVP stage:
01 - AI Outfit Advice
You can enter a sentence or upload a photo to generate your personalized outfit inspiration images.
02 - One Item, Multiple Styles
Get multiple outfit ideas from your uploaded item — expand your wardrobe without extra cost.
03 - Virtual Try-On
Offer a virtual try-on experience for the generated outfits, helping you explore different styles at a low cost.
Business Goals
During the MVP stage, we set the following business goals:
Validate that the AI product’s key features satisfies basic user needs.
Track registrations, active users (DAU/MAU), and retention rates to evaluate user adoption of the product.
process
The design process followed the Double Diamond framework, iteratively refining our research and designs to address users’ needs.
01
02
03
04
Discovery
Define
Develop
Deliver
Discovery 🔍
Desk Research & Competitive Analysis:
Validate that the AI product’s key features satisfies basic user needs.
Most existing solutions focus on wardrobe management or basic recommendations, offering limited personalization, low engagement, and fragmented user experiences.
AI-driven styling represents a high-potential blue ocean, with technical and personalization challenges, ideal for testing product-market fit.
Define 🎯
Persona
Information Architecture
Develop 🎨
User Flows
1 - New User Flow
Access app (not logged in) → Browse or try to generate → Log in to continue the experience
Designed a soft onboarding experience that encourages users to explore outfit inspirations first, then sign up or log in when engaging with core features.
2 - Core Flow A
Don‘t know what to wear?
Enter prompts or explore → Get oufit inspiration → Virtual try-on → Like / Share / Buy
3 - Core Flow B
Don‘t know how to style your clothes?
Upload a clothing item → Get oufit inspiration → Virtual try-on → Like / Share / Buy
4 - Additional Flow
Want more personalized recommendations?
Manage personal style profile & outfit history - Build personalized recommendations and track styling journey.
Deliver 📦
Designed 25+ MVP screens, created high-fidelity prototypes, and built a design system with 70+ reusable components.
Annotated design files for efficient handoff and collaborated closely with engineers to ensure smooth implementation.
Participated in weekly design reviews, working cross-functionally to deliver 0→1 design solutions and complete two full design iterations.
Design System
Responsive Design (APP/WEB)
Outcome / Impact
The MVP launch delivered encouraging results, validating both the product concept and design direction — even without large-scale promotion! ✨
The bounce rate of the MVP was around 20%, well below the industry’s healthy benchmark (~40%).
Users proactively shared positive feedback, praising the “refined and aesthetic design.”
The longest single-day usage exceeded 2 hours, indicating strong engagement and user interest.
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